{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(213605, 30) (71202, 30) (213605,) (71202,)\n",
      "(426448, 30) (426448,)\n",
      "213224 213224\n",
      "[[70155   936]\n",
      " [   19    92]]\n",
      "0.986587455409\n",
      "0.0894941634241\n",
      "0.828828828829\n",
      "0.945549423331\n"
     ]
    },
    {
     "data": {
      "image/png": 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LfCiJvioiIhIfJSxSNpo6H8qmm+YvFhHJUWK22qOPDvOqSNlQwiIlLZsWk6bM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\ndhhdAVhImm+zXcXy7ei7z6/0UX5pRKwc3OY1VT2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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16132f69668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "#importing the data set\n",
    "df=pd.read_csv(\"C:/Users/sethi/Desktop/Fall 2016/Practice/Kaggle/Credit Card Fraud/Data set.csv\")\n",
    "\n",
    "#creating target series\n",
    "target=df['Class']\n",
    "target\n",
    "\n",
    "#dropping the target variable from the data set\n",
    "df.drop('Class',axis=1,inplace=True)\n",
    "df.shape\n",
    "\n",
    "#converting them to numpy arrays\n",
    "X=np.array(df)\n",
    "y=np.array(target)\n",
    "X.shape\n",
    "y.shape\n",
    "\n",
    "#distribution of the target variable\n",
    "len(y[y==1])\n",
    "len(y[y==0])\n",
    "\n",
    "#splitting the data set into train and test (75:25)\n",
    "from sklearn.cross_validation import train_test_split\n",
    "X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.25,random_state=1)\n",
    "print(X_train.shape,X_test.shape,y_train.shape,y_test.shape)\n",
    "\n",
    "#applyting SMOTE to oversample the minority class\n",
    "from imblearn.over_sampling import SMOTE\n",
    "sm=SMOTE(random_state=2)\n",
    "X_sm,y_sm=sm.fit_sample(X_train,y_train)\n",
    "print(X_sm.shape,y_sm.shape)\n",
    "print(len(y_sm[y_sm==1]),len(y_sm[y_sm==0]))\n",
    "\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import metrics\n",
    "\n",
    "#Logistic Regression\n",
    "logreg=LogisticRegression()\n",
    "logreg.fit(X_sm,y_sm)\n",
    "y_logreg=logreg.predict(X_test)\n",
    "y_logreg_prob=logreg.predict_proba(X_test)[:,1]\n",
    "\n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test,y_logreg))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test,y_logreg))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test,y_logreg))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test,y_logreg))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test,y_logreg_prob))\n",
    "auc=metrics.roc_auc_score(y_test,y_logreg_prob)\n",
    "\n",
    "#plotting the ROC curve\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_logreg_prob)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Receiver Operating Characteristic\\nLogistic Regression')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[67323  3768]\n",
      " [   62    49]]\n",
      "0.946209376141\n",
      "0.0128373067854\n",
      "0.441441441441\n",
      "0.7110521713\n"
     ]
    },
    {
     "data": {
      "image/png": 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NhIW2aQhHFiRJqryaCAtOQ0iSVJyaCAsucJQkqTg1ERYcWZAkqTg1FRYcWZAk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x161323aeb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#K Nearest Neighbors\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn=KNeighborsClassifier(n_neighbors=5)\n",
    "knn.fit(X_sm,y_sm)\n",
    "y_knn=knn.predict(X_test)\n",
    "y_knn_prob=knn.predict_proba(X_test)[:,1]\n",
    "\n",
    "#metrics evaluation\n",
    "print(metrics.confusion_matrix(y_test,y_knn))\n",
    "print(metrics.accuracy_score(y_test,y_knn))\n",
    "print(metrics.precision_score(y_test,y_knn))\n",
    "print(metrics.recall_score(y_test,y_knn))\n",
    "print(metrics.roc_auc_score(y_test,y_knn_prob))\n",
    "\n",
    "#plotting the ROC curve\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_knn_prob)\n",
    "plt.plot(fpr,tpr)\n",
    "plt.xlim([0.0,1.0])\n",
    "plt.ylim([0.0,1.0])\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[71076    15]\n",
      " [   23    88]]\n",
      "0.999466307126\n",
      "0.854368932039\n",
      "0.792792792793\n",
      "0.935089856282\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16133176a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[71076    15]\n",
      " [   23    88]]\n",
      "Accuracy:\n",
      " 0.999466307126\n",
      "Precision:\n",
      " 0.854368932039\n",
      "Recall:\n",
      " 0.792792792793\n",
      "AUC:\n",
      " 0.935089856282\n"
     ]
    },
    {
     "data": {
      "image/png": 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jQBYvhpUra59jiy1qJhlf/3r6BKRHD3XDiLRYzz8Phx4Ku+0GM2aEv1akRVHC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\nIi1Ip06dmDt3bpMmLUpYRESK2NKlS1m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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16132e8cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf=RandomForestClassifier(random_state=3)\n",
    "rf.fit(X_sm,y_sm)\n",
    "y_rf=rf.predict(X_test)\n",
    "y_rf_prob=rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test,y_rf))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test,y_rf))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test,y_rf))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test,y_rf))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test,y_rf_prob))\n",
    "auc=metrics.roc_auc_score(y_test,y_rf_prob)\n",
    "\n",
    "#plotting the ROC curve\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_rf_prob)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Receiver Operating Characteristic\\nRandom Forest')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      " [[71074    17]\n",
      " [   23    88]]\n",
      "Accuracy:\n",
      " 0.999438218028\n",
      "Precision:\n",
      " 0.838095238095\n",
      "Recall:\n",
      " 0.792792792793\n",
      "AUC:\n",
      " 0.917155476783\n"
     ]
    },
    {
     "data": {
      "image/png": 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gZEQkjX33hd/8Bn74w9DnKSVHCYtIisQmeQ0lI9lskpfortEmeSLN0K4dnHtu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\nwHZquU+RdRilWzn8A3BiRCwsuz3NImkz0i0sLo6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x16132eebda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Random Forest\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf=RandomForestClassifier(criterion='entropy',random_state=3)\n",
    "rf.fit(X_sm,y_sm)\n",
    "y_rf=rf.predict(X_test)\n",
    "y_rf_prob=rf.predict_proba(X_test)[:,1]\n",
    "\n",
    "#Performance metrics evaluation\n",
    "print(\"Confusion Matrix:\\n\",metrics.confusion_matrix(y_test,y_rf))\n",
    "print(\"Accuracy:\\n\",metrics.accuracy_score(y_test,y_rf))\n",
    "print(\"Precision:\\n\",metrics.precision_score(y_test,y_rf))\n",
    "print(\"Recall:\\n\",metrics.recall_score(y_test,y_rf))\n",
    "print(\"AUC:\\n\",metrics.roc_auc_score(y_test,y_rf_prob))\n",
    "auc=metrics.roc_auc_score(y_test,y_rf_prob)\n",
    "\n",
    "#plotting the ROC curve\n",
    "fpr,tpr,thresholds=metrics.roc_curve(y_test,y_rf_prob)\n",
    "plt.plot(fpr,tpr,'b', label='AUC = %0.2f'% auc)\n",
    "plt.plot([0,1],[0,1],'r-.')\n",
    "plt.xlim([-0.2,1.2])\n",
    "plt.ylim([-0.2,1.2])\n",
    "plt.title('Receiver Operating Characteristic\\nRandom Forest')\n",
    "plt.legend(loc='lower right')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
